Abstract: This disclosure is directed to methods, computer program products, and systems for providing surface vehicle tracking data, including indications of potential collision zones, to an airport map display system onboard an aircraft. In one example, a method includes identifying historical navigation route data, aerodrome guidance features, and a predicted path of a first vehicle. The method further includes determining predicted positions along the predicted path and determining predicted positions of a second vehicle and comparing vehicle envelopes for the two vehicles to determine a predicted collision zone of the vehicles.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which determine jitter buffer delay by inputting jitter buffer and currently observed network status information to a machine learned model that is trained using a reinforcement learning (RL) method. The model maps these inputs to an action to compress, stretch, or hold the jitter buffer delay, which is used by a recipient computing device to optimize the jitter buffer delay. The model may be trained using a simulator that uses network traces of past real streaming sessions (e.g., communication sessions) of users. By training the model through reinforcement learning, the model learns to make better decisions through reinforcement in the form of reward signals that reflect the performance of each decision.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which determine jitter buffer delay by inputting jitter buffer and currently observed network status information to a machine learned model that is trained using a reinforcement learning (RL) method. The model maps these inputs to an action to compress, stretch, or hold the jitter buffer delay, which is used by a recipient computing device to optimize the jitter buffer delay. The model may be trained using a simulator that uses network traces of past real streaming sessions (e.g., communication sessions) of users. By training the model through reinforcement learning, the model learns to make better decisions through reinforcement in the form of reward signals that reflect the performance of each decision.

Abstract: A wireless communication system for exchanging messages between a patient support apparatus and a headwall unit includes intelligence built into either or both of these devices (and/or a server) for detecting when a mismatch occurs between the communication capabilities of the patient support apparatus and those of the headwall unit. A warning is issued in those situations where a caregiver is relying on communication abilities that are not present in one or both of the devices. Examples of such situations include, but are not limited to, a nurse call cable not being coupled between the patient support apparatus and the nurse call system when the patient support apparatus does not have the capability to wirelessly communicate with the nurse call system. Alternatively, or additionally, the headwall unit may issue an alert to a nurse call system if it detects one or more conditions that warrant immediate caregiver attention.

Abstract: This disclosure is directed to methods, computer program products, and systems for providing surface vehicle tracking data, including indications of potential collision zones, to an airport map display system onboard an aircraft. In one example, a method includes identifying historical navigation route data, aerodrome guidance features, and a predicted path of a first vehicle. The method further includes determining predicted positions along the predicted path and determining predicted positions of a second vehicle and comparing vehicle envelopes for the two vehicles to determine a predicted collision zone of the vehicles.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which determine jitter buffer delay by inputting jitter buffer and currently observed network status information to a machine learned model that is trained using a reinforcement learning (RL) method. The model maps these inputs to an action to compress, stretch, or hold the jitter buffer delay, which is used by a recipient computing device to optimize the jitter buffer delay. The model may be trained using a simulator that uses network traces of past real streaming sessions (e.g., communication sessions) of users. By training the model through reinforcement learning, the model learns to make better decisions through reinforcement in the form of reward signals that reflect the performance of each decision.

Abstract: Techniques are described for managing synchronized jitter buffers for streaming data (e.g., for real-time audio and/or video communications). A separate jitter buffer can be maintained for each codec. For example, as data is received in network packets, the data is added to the jitter buffer corresponding to the codec that is associated with the received data. When data needs to be read, the same amount of data is read from each of the jitter buffers. In other words, at each instance where data needs to be obtained (e.g., for decoding and playback), the same amount of data is obtained from each of the jitter buffers. In addition, the multiple jitter buffers use the same playout timestamp that is synchronized across the multiple of jitter buffers.

Abstract: Techniques are described for managing synchronized jitter buffers for streaming data (e.g., for real-time audio and/or video communications). A separate jitter buffer can be maintained for each codec. For example, as data is received in network packets, the data is added to the jitter buffer corresponding to the codec that is associated with the received data. When data needs to be read, the same amount of data is read from each of the jitter buffers. In other words, at each instance where data needs to be obtained (e.g., for decoding and playback), the same amount of data is obtained from each of the jitter buffers. In addition, the multiple jitter buffers use the same playout timestamp that is synchronized across the multiple of jitter buffers.

Abstract: Example apparatus and methods concern performing stereo acoustic echo cancellation using a correlation based filter adaptation control approach and without using stereo de-correlation. An embodiment includes a stereo adaptive filter that produces an echo removed microphone signal from received audio signals. The embodiment includes a mono adaptive filter that produces an echo removed microphone signal from the received audio signals. A correlation detector determines a level of correlation between the received signals and provides a signal to an adaptive filter controller. The adaptive filter controller controls how the stereo adaptive filter and the mono adaptive filter adapt audio echo cancellation as a function of the correlation between the received signals. A signal selector may select for output the signal from either the stereo adaptive filter or the mono adaptive filter based, for example, on the power level of the signals.

Abstract: Example apparatus and methods concern performing stereo acoustic echo cancellation using a correlation based filter adaptation control approach and without using stereo de-correlation. An embodiment includes a stereo adaptive filter that produces an echo removed microphone signal from received audio signals. The embodiment includes a mono adaptive filter that produces an echo removed microphone signal from the received audio signals. A correlation detector determines a level of correlation between the received signals and provides a signal to an adaptive filter controller. The adaptive filter controller controls how the stereo adaptive filter and the mono adaptive filter adapt audio echo cancellation as a function of the correlation between the received signals. A signal selector may select for output the signal from either the stereo adaptive filter or the mono adaptive filter based, for example, on the power level of the signals.

Abstract: Architecture that mitigates echo in voice communications using echo detection and adaptive management of attenuation by a non-linear processor (NLP). Suppression values provided by the NLP are determined based on echo detection and retained on a case-by-case basis to automatically increase or decrease the attenuation as needed. Feedback is incorporated that where the controls for the NLP attenuation is given by the amount of echo that remains, and this in turn affects the amount of echo that remains.

Abstract: Mitigation of nonlinear echoes by dynamic range compression may be provided. An audio output may be evaluated to determine whether nonlinear echoes are interfering with an audio signal. In order to reduce such nonlinear echoes, a dynamic range compression algorithm may be applied to the audio signal prior to output. The signal may be modified according to a compression wave and/or subjected to an attenuation factor.

Abstract: The detection of double-talk in audio communication is provided. A communication device may receive an echo signal mixed with a speech signal at a near end location. The echo signal may be generated by speech transmitted by a remote party at a far end location to a local party at the near end location. The speech signal may be received from the local party for transmission to the remote party. The communication device may then filter the echo signal and the speech signal. The communication device may then analyze the speech signal to identify speech characteristics which indicate the presence of double-talk. The communication device may then set a flag upon identifying the speech characteristics which indicate the presence of the double-talk. The communication device may then process the filtered signals to further suppress remaining echo prior to transmission of the speech signal to the remote party.

Abstract: Presence of echo in a conversation is determined through a search performed in the sub-band domain using independent short length adaptive filters across a range of sub-band and maximum expected echo delay values. The adaptive filters attempt to predict and cancel the echo in the microphone signal based on the content in the speaker signal. If substantial cancellation is achieved in any of the sub-band filters, echo presence in the microphone signal is determined.

Abstract: Detecting at least one of an echo detector and a noise detector based on analysis of audio streams transmitted to and received from each endpoint of a conference. When certain characteristics of the respective audio streams for a given endpoint are classified as significant against certain criteria, a determination is made as to whether that endpoint is a source of echo and/or noise. Subsequent actions are taken to alert users and/or prevent broadcast of impaired signals.

Abstract: A voice communication end device provides echo reduction when operating in a full duplex mode using acoustic echo cancellation, which includes periodic audio queue adjustments to account for drift. The end device performs a quality assessment by calculating consistency statistics for the queue adjustments to determine whether acoustic echo cancellation would be ineffective, and if so falls back to a half duplex mode using voice switching.

Abstract: Detecting at least one of an echo detector and a noise detector based on analysis of audio streams transmitted to and received from each endpoint of a conference. When certain characteristics of the respective audio streams for a given endpoint are classified as significant against certain criteria, a determination is made as to whether that endpoint is a source of echo and/or noise. Subsequent actions are taken to alert users and/or prevent broadcast of impaired signals.

Abstract: In one embodiment, a two-way telecommunication device may perform acoustic echo cancellation on incoming signals. An audio decoding module may produce an audio render signal. An audio capture interface may receive an audio capture signal. A short length adaptive filter may determine a time delay between the audio render signal and the audio capture signal by adaptively predicting a sub-band of the audio capture signal using a corresponding sub-band of the audio render signal.

Abstract: Mitigation of nonlinear echoes by dynamic range compression may be provided. An audio output may be evaluated to determine whether nonlinear echoes are interfering with an audio signal. In order to reduce such nonlinear echoes, a dynamic range compression algorithm may be applied to the audio signal prior to output. The signal may be modified according to a compression wave and/or subjected to an attenuation factor.

Abstract: Architecture that mitigates echo in voice communications using echo detection and adaptive management of attenuation by a non-linear processor (NLP). Suppression values provided by the NLP are determined based on echo detection and retained on a case-by-case basis to automatically increase or decrease the attenuation as needed. Feedback is incorporated that where the controls for the NLP attenuation is given by the amount of echo that remains, and this in turn affects the amount of echo that remains.